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Related Experiment Video

Updated: Sep 11, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Frequency Switching Neuristor for Realizing Intrinsic Plasticity and Enabling Robust Neuromorphic Computing.

Woojoon Park1, Hanchan Song1, Eun Young Kim1,2

  • 1Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.

Advanced Materials (Deerfield Beach, Fla.)
|August 18, 2025
PubMed
Summary

Researchers developed a novel frequency switching (FS) neuristor that mimics intrinsic plasticity in artificial neurons. This innovation enhances neuromorphic computing by improving network performance and resilience.

Keywords:
brain‐inspired computingintrinsic plasticitymott memristoroscillationsparse neural network

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Area of Science:

  • Neuroscience
  • Materials Science
  • Computer Engineering

Background:

  • The human brain's adaptability relies on spatiotemporal spiking and intrinsic plasticity, where neurons adjust their excitability.
  • Mott memristors act as artificial neurons (neuristors) for spiking activity, but intrinsic plasticity in neuromorphic computing remains underexplored.

Purpose of the Study:

  • To introduce a frequency switching (FS) neuristor that emulates neuronal intrinsic plasticity.
  • To investigate the role and benefits of intrinsic plasticity in neuromorphic computing systems.

Main Methods:

  • A frequency switching (FS) neuristor was created by combining a volatile Mott memristor with a non-volatile valence change memory (VCM) memristor.
  • Device-based simulations of sparse neural networks were performed to evaluate the FS neuristor's capabilities.

Main Results:

  • The FS neuristor demonstrated programmable multi-level frequency-voltage (f-V) characteristics, mimicking intrinsic neuronal plasticity.
  • Simulations indicated that intrinsic plasticity functions as integrated memory and processing, boosting network performance and reducing energy use.
  • The network exhibited structural plasticity, recovering performance after simulated neuron damage.

Conclusions:

  • The developed FS neuristor effectively emulates intrinsic plasticity, offering a new pathway for advanced neuromorphic systems.
  • Intrinsic plasticity in neuromorphic computing enhances efficiency, resilience, and adaptability, paving the way for more robust AI hardware.